Perbandingan Performa Kombinasi Algoritma Pengurutan Quick-Insertion Sort dan Merge-Insertion Sort

Pengurutan merupakan bagian penting dalam pengolahan data. Data yang terurut memudahkan dalam pencarian data. Algoritma pengurutan hanya cocok untuk permasalahan dengan karakteristik terntentu. Algoritma pengurutan yang cocok digunakan untuk data dalam skala besar yaitu Quick Sort dan Merge Sort namun untuk data dalam skala kecil algoritma Insertion Sort lebih cocok. Karakteristik Quick Sort dan Merge Sort yang membagi-bagi data ke dalam bagian dan setiap bagian menjadi sub-bagian maka akan didapat data dalam ukuran kecil. Proses pengurutan sub-bagian dapat digantikan dengan menggunakan Insertion Sort. Kombinasi algoritma Quick-Insertion Sort memiliki performa yang lebih baik dibandingkan dengan Quick Sort sendiri dan Merge-Insertion Sort memiliki performa yang lebih baik dibandingkan dengan Merge Sort sendiri. Quick-Insertion Sort 15% lebih cepat dibandingkan dengan Quick Sort dengan batas 16. Merge-Insertion Sort lebih cepat 34,8% lebih cepat dibandingkan dengan Merge Sort dengan batas 16

Enhanced Relative Comparison of Traditional Sorting Approaches towards Optimization of New Hybrid Two-in-One (OHTO) Novel Sorting Technique

In the world of computer technology, sorting is an operation on a data set that involves ordering it in an increasing or decreasing fashion according to some linear relationship among the data items. With the rise in the generation of big data, the concept of big numbers has come into existence. When the number of records to be sorted is limited to thousands, traditional sorting approaches can be used; in such cases, complexities in their execution time can be ignored. However, in the case of big data, where processing times for billions or trillions of records are very long, time complexity is very significant. Therefore, an optimized sorting technique with efficient time complexity is very much required. Hence, in this paper an optimized sorting technique is proposed, named Optimized Hybrid Two-in-One Novel Sorting Technique (OHTO, a mixed approach of the Insertion Sort technique and the Bubble Sort technique. The proposed sorting technique uses the procedure of both Bubble Sort and Insertion Sort, resulting in fewer comparisons, fewer data movements, fewer data insertions, and less time complexity for any given input data set compared to existing sorting techniques

Enhanced Relative Comparison of Traditional Sorting Approaches towards Optimization of New Hybrid Two-in-One (OHTO) Novel Sorting Technique

In the world of computer technology, sorting is an operation on a data set that involves ordering it in an increasing or decreasing fashion according to some linear relationship among the data items. With the rise in the generation of big data, the concept of big numbers has come into existence. When the number of records to be sorted is limited to thousands, traditional sorting approaches can be used; in such cases, complexities in their execution time can be ignored. However, in the case of big data, where processing times for billions or trillions of records are very long, time complexity is very significant. Therefore, an optimized sorting technique with efficient time complexity is very much required. Hence, in this paper an optimized sorting technique is proposed, named Optimized Hybrid Two-in-One Novel Sorting Technique (OHTO, a mixed approach of the Insertion Sort technique and the Bubble Sort technique. The proposed sorting technique uses the procedure of both Bubble Sort and Insertion Sort, resulting in fewer comparisons, fewer data movements, fewer data insertions, and less time complexity for any given input data set compared to existing sorting techniques

Comparison of sorting algorithms

Važnost algoritama sortiranja je velika jer se koriste svakodnevno. Primjerice, prilikom sortiranja datoteka na računalu njihov redoslijed može biti određen prema nazivu, datumu, veličini i sl. Postoje razni algoritmi sortiranja koji se razlikuju prema određenim karakteristikama. Kako bi se utvrdilo koji je bolji, odnosno kojemu se mogu poboljšati performanse, potrebno ih je analizirati, točnije procijeniti potrebne resurse. Takva se procjena naziva „a priori“ analiza složenosti, dok stvarni izračun pripada „a posteriori“ analizi. Dva osnovna resursa algoritama su prostor i vrijeme. Procjena potrebnog vremena algoritmu za rješavanje definiranog problema se izražava kroz funkciju T(n) koja nije u potpunosti točna jer ne može odrediti stvarno vrijeme izvršavanja algoritma. Stoga, stvarno vrijeme izraženo u vremenskim jedinicama točnije određuje resurse. Usporedbom prema navedenim resursima je moguće utvrditi koji algoritam je bolji od ostalih, ali za određene situacije sortiranja koje ovise o početnom redoslijedu sadržaja, veličini i sl.The importance of sorting algorithms is significant because they are used on a regular basis. For instance, while sorting files on a computer, their sequence can be determined by name, date, size etc. There are a lot of sorting algorithms which differ from each other by certain characteristics. In order to determine which of them is better or whether some of them need their performances improved, they have to be analyzed and the needed resources have to be estimated. That kind of estimation is called „a priori“ complexity analysis, whereas the real calculation belong to the „a posteriori“ one. Two basic algorithm resources are space and time. The estimation of the time required for the algorithm to resolve a specifically defined problem is shown through the T(n) function. This function isn't completely accurate because it can not define the real time of the algorithm implementation. Therefore, the real time, which is defined in time units, specifies resources more accurately. With the comparison towards the given resources, it is possible to define which algorithm is better but only for certain situations of sorting which depend on the initial sequence on content, size etc

Comparison of sorting algorithms

Važnost algoritama sortiranja je velika jer se koriste svakodnevno. Primjerice, prilikom sortiranja datoteka na računalu njihov redoslijed može biti određen prema nazivu, datumu, veličini i sl. Postoje razni algoritmi sortiranja koji se razlikuju prema određenim karakteristikama. Kako bi se utvrdilo koji je bolji, odnosno kojemu se mogu poboljšati performanse, potrebno ih je analizirati, točnije procijeniti potrebne resurse. Takva se procjena naziva „a priori“ analiza složenosti, dok stvarni izračun pripada „a posteriori“ analizi. Dva osnovna resursa algoritama su prostor i vrijeme. Procjena potrebnog vremena algoritmu za rješavanje definiranog problema se izražava kroz funkciju T(n) koja nije u potpunosti točna jer ne može odrediti stvarno vrijeme izvršavanja algoritma. Stoga, stvarno vrijeme izraženo u vremenskim jedinicama točnije određuje resurse. Usporedbom prema navedenim resursima je moguće utvrditi koji algoritam je bolji od ostalih, ali za određene situacije sortiranja koje ovise o početnom redoslijedu sadržaja, veličini i sl.The importance of sorting algorithms is significant because they are used on a regular basis. For instance, while sorting files on a computer, their sequence can be determined by name, date, size etc. There are a lot of sorting algorithms which differ from each other by certain characteristics. In order to determine which of them is better or whether some of them need their performances improved, they have to be analyzed and the needed resources have to be estimated. That kind of estimation is called „a priori“ complexity analysis, whereas the real calculation belong to the „a posteriori“ one. Two basic algorithm resources are space and time. The estimation of the time required for the algorithm to resolve a specifically defined problem is shown through the T(n) function. This function isn't completely accurate because it can not define the real time of the algorithm implementation. Therefore, the real time, which is defined in time units, specifies resources more accurately. With the comparison towards the given resources, it is possible to define which algorithm is better but only for certain situations of sorting which depend on the initial sequence on content, size etc